# HG changeset patch # User fubar # Date 1421723460 18000 # Node ID a72211e03b3883725237d6cfb791e21fb3d188d6 # Parent 7e1ae353a1c8b538b5093395131f1801a184c81f Uploaded diff -r 7e1ae353a1c8 -r a72211e03b38 hairpinTool.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/hairpinTool.R Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,1069 @@ +#!/usr/bin/env Rscript +# ARGS: 1.inputType -String specifying format of input (fastq or table) +# IF inputType is "fastq" or "pairedFastq: +# 2*.fastqPath -One or more strings specifying path to fastq files +# 2.annoPath -String specifying path to hairpin annotation table +# 3.samplePath -String specifying path to sample annotation table +# 4.barStart -Integer specifying starting position of barcode +# 5.barEnd -Integer specifying ending position of barcode +# ### +# IF inputType is "pairedFastq": +# 6.barStartRev -Integer specifying starting position of barcode +# on reverse end +# 7.barEndRev -Integer specifying ending position of barcode +# on reverse end +# ### +# 8.hpStart -Integer specifying startins position of hairpin +# unique region +# 9.hpEnd -Integer specifying ending position of hairpin +# unique region +# IF inputType is "counts": +# 2.countPath -String specifying path to count table +# 3.annoPath -String specifying path to hairpin annotation table +# 4.samplePath -String specifying path to sample annotation table +# ### +# 10.secFactName -String specifying name of secondary factor +# 11.cpmReq -Float specifying cpm requirement +# 12.sampleReq -Integer specifying cpm requirement +# 13.readReq -Integer specifying read requirement +# 14.fdrThresh -Float specifying the FDR requirement +# 15.lfcThresh -Float specifying the log-fold-change requirement +# 16.workMode -String specifying exact test or GLM usage +# 17.htmlPath -String specifying path to HTML file +# 18.folderPath -String specifying path to folder for output +# IF workMode is "classic" (exact test) +# 19.pairData[2] -String specifying first group for exact test +# 20.pairData[1] -String specifying second group for exact test +# ### +# IF workMode is "glm" +# 19.contrastData -String specifying contrasts to be made +# 20.roastOpt -String specifying usage of gene-wise tests +# 21.hairpinReq -String specifying hairpin requirement for gene- +# wise test +# 22.selectOpt -String specifying type of selection for barcode +# plots +# 23.selectVals -String specifying members selected for barcode +# plots +# ### +# +# OUT: Bar Plot of Counts Per Index +# Bar Plot of Counts Per Hairpin +# MDS Plot +# BCV Plot +# Smear Plot +# Barcode Plots (If Genewise testing was selected) +# Top Expression Table +# Feature Counts Table +# HTML file linking to the ouputs +# +# Author: Shian Su - registertonysu@gmail.com - Jan 2014 + +# Record starting time +timeStart <- as.character(Sys.time()) +options(bitmapType='cairo') +# needed to prevent missing x11 errors for png() +# Loading and checking required packages +library(methods, quietly=TRUE, warn.conflicts=FALSE) +library(statmod, quietly=TRUE, warn.conflicts=FALSE) +library(splines, quietly=TRUE, warn.conflicts=FALSE) +library(edgeR, quietly=TRUE, warn.conflicts=FALSE) +library(limma, quietly=TRUE, warn.conflicts=FALSE) + +if (packageVersion("edgeR") < "3.7.17") { + stop("Please update 'edgeR' to version >= 3.7.17 to run this tool") +} + +if (packageVersion("limma")<"3.21.16") { + message("Update 'limma' to version >= 3.21.16 to see updated barcode graphs") +} + +################################################################################ +### Function declarations +################################################################################ + +# Function to load libaries without messages +silentLibrary <- function(...) { + list <- c(...) + for (package in list){ + suppressPackageStartupMessages(library(package, character.only=TRUE)) + } +} + +# Function to sanitise contrast equations so there are no whitespaces +# surrounding the arithmetic operators, leading or trailing whitespace +sanitiseEquation <- function(equation) { + equation <- gsub(" *[+] *", "+", equation) + equation <- gsub(" *[-] *", "-", equation) + equation <- gsub(" *[/] *", "/", equation) + equation <- gsub(" *[*] *", "*", equation) + equation <- gsub("^\\s+|\\s+$", "", equation) + return(equation) +} + +# Function to sanitise group information +sanitiseGroups <- function(string) { + string <- gsub(" *[,] *", ",", string) + string <- gsub("^\\s+|\\s+$", "", string) + return(string) +} + +# Function to change periods to whitespace in a string +unmake.names <- function(string) { + string <- gsub(".", " ", string, fixed=TRUE) + return(string) +} + +# Function has string input and generates an output path string +makeOut <- function(filename) { + return(paste0(folderPath, "/", filename)) +} + +# Function has string input and generates both a pdf and png output strings +imgOut <- function(filename) { + assign(paste0(filename, "Png"), makeOut(paste0(filename,".png")), + envir=.GlobalEnv) + assign(paste0(filename, "Pdf"), makeOut(paste0(filename,".pdf")), + envir=.GlobalEnv) +} + +# Create cat function default path set, default seperator empty and appending +# true by default (Ripped straight from the cat function with altered argument +# defaults) +cata <- function(..., file=htmlPath, sep="", fill=FALSE, labels=NULL, + append=TRUE) { + if (is.character(file)) + if (file == "") + file <- stdout() + else if (substring(file, 1L, 1L) == "|") { + file <- pipe(substring(file, 2L), "w") + on.exit(close(file)) + } + else { + file <- file(file, ifelse(append, "a", "w")) + on.exit(close(file)) + } + .Internal(cat(list(...), file, sep, fill, labels, append)) +} + +# Function to write code for html head and title +HtmlHead <- function(title) { + cata("\n") + cata("", title, "\n") + cata("\n") +} + +# Function to write code for html links +HtmlLink <- function(address, label=address) { + cata("", label, "
\n") +} + +# Function to write code for html images +HtmlImage <- function(source, label=source, height=600, width=600) { + cata("\"",\n") +} + +# Function to write code for html list items +ListItem <- function(...) { + cata("
  • ", ..., "
    • \n") +} + +TableItem <- function(...) { + cata("", ..., "\n") +} + +TableHeadItem <- function(...) { + cata("", ..., "\n") +} +################################################################################ +### Input Processing +################################################################################ + +# Grabbing arguments from command line +argv <- commandArgs(T) + +inputType <- as.character(argv[1]) +if (inputType == "fastq") { + + fastqPath <- as.character(gsub("fastq::", "", argv[grepl("fastq::", argv)], + fixed=TRUE)) + + # Remove fastq paths + argv <- argv[!grepl("fastq::", argv, fixed=TRUE)] + + fastqPathRev <- NULL + annoPath <- as.character(argv[2]) + samplePath <- as.character(argv[3]) + barStart <- as.numeric(argv[4]) + barEnd <- as.numeric(argv[5]) + barStartRev <- NULL + barStartRev <- NULL + hpStart <- as.numeric(argv[8]) + hpEnd <- as.numeric(argv[9]) +} else if (inputType=="pairedFastq") { + + fastqPath <- as.character(gsub("fastq::", "", argv[grepl("fastq::", argv)], + fixed=TRUE)) + + fastqPathRev <- as.character(gsub("fastqRev::", "", + argv[grepl("fastqRev::", argv)], fixed=TRUE)) + + # Remove fastq paths + argv <- argv[!grepl("fastq::", argv, fixed=TRUE)] + argv <- argv[!grepl("fastqRev::", argv, fixed=TRUE)] + + annoPath <- as.character(argv[2]) + samplePath <- as.character(argv[3]) + barStart <- as.numeric(argv[4]) + barEnd <- as.numeric(argv[5]) + barStartRev <- as.numeric(argv[6]) + barEndRev <- as.numeric(argv[7]) + hpStart <- as.numeric(argv[8]) + hpEnd <- as.numeric(argv[9]) +} else if (inputType == "counts") { + countPath <- as.character(argv[2]) + annoPath <- as.character(argv[3]) + samplePath <- as.character(argv[4]) +} + +secFactName <- as.character(argv[10]) +cpmReq <- as.numeric(argv[11]) +sampleReq <- as.numeric(argv[12]) +readReq <- as.numeric(argv[13]) +fdrThresh <- as.numeric(argv[14]) +lfcThresh <- as.numeric(argv[15]) +selectDirection <- as.character(argv[16]) +workMode <- as.character(argv[17]) +htmlPath <- as.character(argv[18]) +folderPath <- as.character(argv[19]) + +if (workMode == "classic") { + pairData <- character() + pairData[2] <- as.character(argv[20]) + pairData[1] <- as.character(argv[21]) +} else if (workMode == "glm") { + contrastData <- as.character(argv[20]) + roastOpt <- as.character(argv[21]) + hairpinReq <- as.numeric(argv[22]) + selectOpt <- as.character(argv[23]) + selectVals <- as.character(argv[24]) +} + +# Read in inputs + +samples <- read.table(samplePath, header=TRUE, sep="\t") + +anno <- read.table(annoPath, header=TRUE, sep="\t") + +if (inputType == "counts") { + counts <- read.table(countPath, header=TRUE, sep="\t") +} + +###################### Check inputs for correctness ############################ +samples$ID <- make.names(samples$ID) + +if ( !any(grepl("group", names(samples))) ) { + stop("'group' column not specified in sample annotation file") +} # Check if grouping variable has been specified + +if (secFactName != "none") { + if ( !any(grepl(secFactName, names(samples))) ) { + tempStr <- paste0("Second factor specified as \"", secFactName, "\" but ", + "no such column name found in sample annotation file") + stop(tempStr) + } # Check if specified secondary factor is present +} + + +if ( any(table(samples$ID) > 1) ){ + tab <- table(samples$ID) + offenders <- paste(names(tab[tab > 1]), collapse=", ") + offenders <- unmake.names(offenders) + stop("'ID' column of sample annotation must have unique values, values ", + offenders, " are repeated") +} # Check that IDs in sample annotation are unique + +if (inputType == "fastq" || inputType == "pairedFastq") { + + if ( any(table(anno$ID) > 1) ){ + tab <- table(anno$ID) + offenders <- paste(names(tab[tab>1]), collapse=", ") + stop("'ID' column of hairpin annotation must have unique values, values ", + offenders, " are repeated") + } # Check that IDs in hairpin annotation are unique + +} else if (inputType == "counts") { + # The first element of the colnames will be 'ID' and should not match + idFromSample <- samples$ID + idFromTable <- colnames(counts)[-1] + if (any(is.na(match(idFromTable, idFromSample)))) { + stop("not all samples have groups specified") + } # Check that a group has be specifed for each sample + + if ( any(table(counts$ID) > 1) ){ + tab <- table(counts$ID) + offenders <- paste(names(tab[tab>1]), collapse=", ") + stop("'ID' column of count table must have unique values, values ", + offenders, " are repeated") + } # Check that IDs in count table are unique +} +if (workMode == "glm") { + if (roastOpt == "yes") { + if (is.na(match("Gene", colnames(anno)))) { + tempStr <- paste("Gene-wise tests selected but'Gene' column not", + "specified in hairpin annotation file") + stop(tempStr) + } + } +} + +if (secFactName != "none") { + if (workMode != "glm") { + tempStr <- paste("only glm analysis type possible when secondary factor", + "used, please change appropriate option.") + } +} + +################################################################################ + +# Process arguments +if (workMode == "glm") { + if (roastOpt == "yes") { + wantRoast <- TRUE + } else { + wantRoast <- FALSE + } +} + +# Split up contrasts seperated by comma into a vector and replace spaces with +# periods +if (exists("contrastData")) { + contrastData <- unlist(strsplit(contrastData, split=",")) + contrastData <- sanitiseEquation(contrastData) + contrastData <- gsub(" ", ".", contrastData, fixed=TRUE) +} + +# Replace spaces with periods in pair data +if (exists("pairData")) { + pairData <- make.names(pairData) +} + +# Generate output folder and paths +dir.create(folderPath, showWarnings=FALSE) + +# Generate links for outputs +imgOut("barHairpin") +imgOut("barIndex") +imgOut("mds") +imgOut("bcv") +if (workMode == "classic") { + smearPng <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").png")) + smearPdf <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").pdf")) + topOut <- makeOut(paste0("toptag(", pairData[2], "-", pairData[1],").tsv")) +} else if (workMode == "glm") { + smearPng <- character() + smearPdf <- character() + topOut <- character() + roastOut <- character() + barcodePng <- character() + barcodePdf <- character() + for (i in 1:length(contrastData)) { + smearPng[i] <- makeOut(paste0("smear(", contrastData[i], ").png")) + smearPdf[i] <- makeOut(paste0("smear(", contrastData[i], ").pdf")) + topOut[i] <- makeOut(paste0("toptag(", contrastData[i], ").tsv")) + roastOut[i] <- makeOut(paste0("gene_level(", contrastData[i], ").tsv")) + barcodePng[i] <- makeOut(paste0("barcode(", contrastData[i], ").png")) + barcodePdf[i] <- makeOut(paste0("barcode(", contrastData[i], ").pdf")) + } +} +countsOut <- makeOut("counts.tsv") +sessionOut <- makeOut("session_info.txt") + +# Initialise data for html links and images, table with the link label and +# link address +linkData <- data.frame(Label=character(), Link=character(), + stringsAsFactors=FALSE) +imageData <- data.frame(Label=character(), Link=character(), + stringsAsFactors=FALSE) + +# Initialise vectors for storage of up/down/neutral regulated counts +upCount <- numeric() +downCount <- numeric() +flatCount <- numeric() + +################################################################################ +### Data Processing +################################################################################ + +# Transform gene selection from string into index values for mroast +if (workMode == "glm") { + if (selectOpt == "rank") { + selectVals <- gsub(" ", "", selectVals, fixed=TRUE) + selectVals <- unlist(strsplit(selectVals, ",")) + + for (i in 1:length(selectVals)) { + if (grepl(":", selectVals[i], fixed=TRUE)) { + temp <- unlist(strsplit(selectVals[i], ":")) + selectVals <- selectVals[-i] + a <- as.numeric(temp[1]) + b <- as.numeric(temp[2]) + selectVals <- c(selectVals, a:b) + } + } + selectVals <- as.numeric(unique(selectVals)) + } else { + selectVals <- gsub(" ", "", selectVals, fixed=TRUE) + selectVals <- unlist(strsplit(selectVals, ",")) + } +} + +if (inputType == "fastq" || inputType == "pairedFastq") { + # Use EdgeR hairpin process and capture outputs + + hpReadout <- capture.output( + data <- processAmplicons(readfile=fastqPath, readfile2=fastqPathRev, + barcodefile=samplePath, + hairpinfile=annoPath, + barcodeStart=barStart, barcodeEnd=barEnd, + barcodeStartRev=barStartRev, + barcodeEndRev=barEndRev, + hairpinStart=hpStart, hairpinEnd=hpEnd, + verbose=TRUE) + ) + + # Remove function output entries that show processing data or is empty + hpReadout <- hpReadout[hpReadout!=""] + hpReadout <- hpReadout[!grepl("Processing", hpReadout)] + hpReadout <- hpReadout[!grepl("in file", hpReadout)] + hpReadout <- gsub(" -- ", "", hpReadout, fixed=TRUE) + + # Make the names of groups syntactically valid (replace spaces with periods) + data$samples$group <- make.names(data$samples$group) + if (secFactName != "none") { + data$samples[[secFactName]] <- make.names(data$samples[[secFactName]]) + } +} else if (inputType == "counts") { + # Process counts information, set ID column to be row names + rownames(counts) <- counts$ID + counts <- counts[ , !(colnames(counts) == "ID")] + countsRows <- nrow(counts) + + # Process group information + sampleNames <- colnames(counts) + matchedIndex <- match(sampleNames, samples$ID) + factors <- samples$group[matchedIndex] + + if (secFactName != "none") { + secFactors <- samples[[secFactName]][matchedIndex] + } + + annoRows <- nrow(anno) + anno <- anno[match(rownames(counts), anno$ID), ] + annoMatched <- sum(!is.na(anno$ID)) + + if (any(is.na(anno$ID))) { + warningStr <- paste("count table contained more hairpins than", + "specified in hairpin annotation file") + warning(warningStr) + } + + # Filter out rows with zero counts + sel <- rowSums(counts)!=0 + counts <- counts[sel, ] + anno <- anno[sel, ] + + # Create DGEList + data <- DGEList(counts=counts, lib.size=colSums(counts), + norm.factors=rep(1,ncol(counts)), genes=anno, group=factors) + + # Make the names of groups syntactically valid (replace spaces with periods) + data$samples$group <- make.names(data$samples$group) +} + +# Filter out any samples with zero counts +if (any(data$samples$lib.size == 0)) { + sampleSel <- data$samples$lib.size != 0 + filteredSamples <- paste(data$samples$ID[!sampleSel], collapse=", ") + data$counts <- data$counts[, sampleSel] + data$samples <- data$samples[sampleSel, ] +} + +# Filter hairpins with low counts +preFilterCount <- nrow(data) +selRow <- rowSums(cpm(data$counts) > cpmReq) >= sampleReq +selCol <- colSums(data$counts) > readReq +data <- data[selRow, selCol] + +# Check if any data survived filtering +if (length(data$counts) == 0) { + stop("no data remains after filtering, consider relaxing filters") +} + +# Count number of filtered tags and samples +postFilterCount <- nrow(data) +filteredCount <- preFilterCount - postFilterCount +sampleFilterCount <- sum(!selCol) + +if (secFactName == "none") { + # Estimate dispersions + data <- estimateDisp(data) + commonBCV <- round(sqrt(data$common.dispersion), 4) +} else { + # Construct design + if (inputType == "counts") { + + sampleNames <- colnames(counts) + matchedIndex <- match(sampleNames, samples$ID) + factors <- factor(make.names(samples$group[matchedIndex])) + + secFactors <- factor(make.names(samples[[secFactName]][matchedIndex])) + + } else if (inputType == "fastq" || inputType == "pairedFastq") { + + factors <- factor(data$sample$group) + secFactors <- factor(data$sample[[secFactName]]) + + } + + design <- model.matrix(~0 + factors + secFactors) + + # Estimate dispersions + data <- estimateDisp(data, design=design) + commonBCV <- round(sqrt(data$common.dispersion), 4) +} + + +################################################################################ +### Output Processing +################################################################################ + +# Plot number of hairpins that could be matched per sample +png(barIndexPng, width=600, height=600) +barplot(height<-colSums(data$counts), las=2, main="Counts per index", + cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2)) +imageData[1, ] <- c("Counts per Index", "barIndex.png") +invisible(dev.off()) + +pdf(barIndexPdf) +barplot(height<-colSums(data$counts), las=2, main="Counts per index", + cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2)) +linkData[1, ] <- c("Counts per Index Barplot (.pdf)", "barIndex.pdf") +invisible(dev.off()) + +# Plot per hairpin totals across all samples +png(barHairpinPng, width=600, height=600) +if (nrow(data$counts)<50) { + barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", + cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2)) +} else { + barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", + cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2), + names.arg=FALSE) +} +imageData <- rbind(imageData, c("Counts per Hairpin", "barHairpin.png")) +invisible(dev.off()) + +pdf(barHairpinPdf) +if (nrow(data$counts)<50) { + barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", + cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2)) +} else { + barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", + cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2), + names.arg=FALSE) +} +newEntry <- c("Counts per Hairpin Barplot (.pdf)", "barHairpin.pdf") +linkData <- rbind(linkData, newEntry) +invisible(dev.off()) + +# Make an MDS plot to visualise relationships between replicate samples +png(mdsPng, width=600, height=600) +plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group), main="MDS Plot") +imageData <- rbind(imageData, c("MDS Plot", "mds.png")) +invisible(dev.off()) + +pdf(mdsPdf) +plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group),main="MDS Plot") +newEntry <- c("MDS Plot (.pdf)", "mds.pdf") +linkData <- rbind(linkData, newEntry) +invisible(dev.off()) + +# BCV Plot +png(bcvPng, width=600, height=600) +plotBCV(data, main="BCV Plot") +imageData <- rbind(imageData, c("BCV Plot", "bcv.png")) +invisible(dev.off()) + +pdf(bcvPdf) +plotBCV(data, main="BCV Plot") +newEntry <- c("BCV Plot (.pdf)", "bcv.pdf") +linkData <- rbind(linkData, newEntry) +invisible(dev.off()) + +if (workMode == "classic") { + # Assess differential representation using classic exact testing methodology + # in edgeR + testData <- exactTest(data, pair=pairData) + + top <- topTags(testData, n=Inf) + + if (selectDirection == "all") { + topIDs <- top$table[(top$table$FDR < fdrThresh) & + (abs(top$table$logFC) > lfcThresh), 1] + } else if (selectDirection == "up") { + topIDs <- top$table[(top$table$FDR < fdrThresh) & + (top$table$logFC > lfcThresh), 1] + } else if (selectDirection == "down") { + topIDs <- top$table[(top$table$FDR < fdrThresh) & + (top$table$logFC < -lfcThresh), 1] +} + + write.table(top, file=topOut, row.names=FALSE, sep="\t") + + linkName <- paste0("Top Tags Table(", pairData[2], "-", pairData[1], + ") (.tsv)") + linkAddr <- paste0("toptag(", pairData[2], "-", pairData[1], ").tsv") + linkData <- rbind(linkData, c(linkName, linkAddr)) + + upCount[1] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh) + + downCount[1] <- sum(top$table$FDR < fdrThresh & + top$table$logFC < -lfcThresh) + + flatCount[1] <- sum(top$table$FDR > fdrThresh | + abs(top$table$logFC) < lfcThresh) + + + + # Select hairpins with FDR < 0.05 to highlight on plot + png(smearPng, width=600, height=600) + plotTitle <- gsub(".", " ", + paste0("Smear Plot: ", pairData[2], "-", pairData[1]), + fixed=TRUE) + plotSmear(testData, de.tags=topIDs, + pch=20, cex=1.0, main=plotTitle) + abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) + imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ")") + imgAddr <- paste0("smear(", pairData[2], "-", pairData[1],").png") + imageData <- rbind(imageData, c(imgName, imgAddr)) + invisible(dev.off()) + + pdf(smearPdf) + plotTitle <- gsub(".", " ", + paste0("Smear Plot: ", pairData[2], "-", pairData[1]), + fixed=TRUE) + plotSmear(testData, de.tags=topIDs, + pch=20, cex=1.0, main=plotTitle) + abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) + imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ") (.pdf)") + imgAddr <- paste0("smear(", pairData[2], "-", pairData[1], ").pdf") + linkData <- rbind(linkData, c(imgName, imgAddr)) + invisible(dev.off()) + +} else if (workMode == "glm") { + # Generating design information + if (secFactName == "none") { + + factors <- factor(data$sample$group) + design <- model.matrix(~0 + factors) + + colnames(design) <- gsub("factors", "", colnames(design), fixed=TRUE) + + } else { + + factors <- factor(data$sample$group) + + if (inputType == "counts") { + + sampleNames <- colnames(counts) + matchedIndex <- match(sampleNames, samples$ID) + factors <- factor(samples$group[matchedIndex]) + + secFactors <- factor(samples[[secFactName]][matchedIndex]) + + } else if (inputType == "fastq" || inputType == "pairedFastq") { + + secFactors <- factor(data$sample[[secFactName]]) + + } + + design <- model.matrix(~0 + factors + secFactors) + + colnames(design) <- gsub("factors", "", colnames(design), fixed=TRUE) + colnames(design) <- gsub("secFactors", secFactName, colnames(design), + fixed=TRUE) + } + + + # Split up contrasts seperated by comma into a vector + contrastData <- unlist(strsplit(contrastData, split=",")) + + for (i in 1:length(contrastData)) { + # Generate contrasts information + contrasts <- makeContrasts(contrasts=contrastData[i], levels=design) + + # Fit negative bionomial GLM + fit <- glmFit(data, design) + # Carry out Likelihood ratio test + testData <- glmLRT(fit, contrast=contrasts) + + # Select hairpins with FDR < 0.05 to highlight on plot + top <- topTags(testData, n=Inf) + + if (selectDirection == "all") { + topIDs <- top$table[(top$table$FDR < fdrThresh) & + (abs(top$table$logFC) > lfcThresh), 1] + } else if (selectDirection == "up") { + topIDs <- top$table[(top$table$FDR < fdrThresh) & + (top$table$logFC > lfcThresh), 1] + } else if (selectDirection == "down") { + topIDs <- top$table[(top$table$FDR < fdrThresh) & + (top$table$logFC < -lfcThresh), 1] + } + + write.table(top, file=topOut[i], row.names=FALSE, sep="\t") + + linkName <- paste0("Top Tags Table(", contrastData[i], ") (.tsv)") + linkAddr <- paste0("toptag(", contrastData[i], ").tsv") + linkData <- rbind(linkData, c(linkName, linkAddr)) + + # Collect counts for differential representation + upCount[i] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh) + downCount[i] <- sum(top$table$FDR < fdrThresh & + top$table$logFC < -lfcThresh) + flatCount[i] <- sum(top$table$FDR > fdrThresh | + abs(top$table$logFC) < lfcThresh) + + # Make a plot of logFC versus logCPM + png(smearPng[i], height=600, width=600) + plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i], + fixed=TRUE)) + plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle) + abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) + + imgName <- paste0("Smear Plot(", contrastData[i], ")") + imgAddr <- paste0("smear(", contrastData[i], ").png") + imageData <- rbind(imageData, c(imgName, imgAddr)) + invisible(dev.off()) + + pdf(smearPdf[i]) + plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i], + fixed=TRUE)) + plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle) + abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) + + linkName <- paste0("Smear Plot(", contrastData[i], ") (.pdf)") + linkAddr <- paste0("smear(", contrastData[i], ").pdf") + linkData <- rbind(linkData, c(linkName, linkAddr)) + invisible(dev.off()) + + genes <- as.character(data$genes$Gene) + unq <- unique(genes) + unq <- unq[!is.na(unq)] + geneList <- list() + for (gene in unq) { + if (length(which(genes == gene)) >= hairpinReq) { + geneList[[gene]] <- which(genes == gene) + } + } + + if (wantRoast) { + # Input preparaton for roast + nrot <- 9999 + set.seed(602214129) + roastData <- mroast(data, index=geneList, design=design, + contrast=contrasts, nrot=nrot) + roastData <- cbind(GeneID=rownames(roastData), roastData) + write.table(roastData, file=roastOut[i], row.names=FALSE, sep="\t") + linkName <- paste0("Gene Level Analysis Table(", contrastData[i], + ") (.tsv)") + linkAddr <- paste0("gene_level(", contrastData[i], ").tsv") + linkData <- rbind(linkData, c(linkName, linkAddr)) + if (selectOpt == "rank") { + selectedGenes <- rownames(roastData)[selectVals] + } else { + selectedGenes <- selectVals + } + + if (packageVersion("limma")<"3.19.19") { + png(barcodePng[i], width=600, height=length(selectedGenes)*150) + } else { + png(barcodePng[i], width=600, height=length(selectedGenes)*300) + } + par(mfrow=c(length(selectedGenes), 1)) + for (gene in selectedGenes) { + barcodeplot(testData$table$logFC, index=geneList[[gene]], + main=paste("Barcode Plot for", gene, "(logFCs)", + gsub(".", " ", contrastData[i])), + labels=c("Positive logFC", "Negative logFC")) + } + imgName <- paste0("Barcode Plot(", contrastData[i], ")") + imgAddr <- paste0("barcode(", contrastData[i], ").png") + imageData <- rbind(imageData, c(imgName, imgAddr)) + dev.off() + if (packageVersion("limma")<"3.19.19") { + pdf(barcodePdf[i], width=8, height=2) + } else { + pdf(barcodePdf[i], width=8, height=4) + } + for (gene in selectedGenes) { + barcodeplot(testData$table$logFC, index=geneList[[gene]], + main=paste("Barcode Plot for", gene, "(logFCs)", + gsub(".", " ", contrastData[i])), + labels=c("Positive logFC", "Negative logFC")) + } + linkName <- paste0("Barcode Plot(", contrastData[i], ") (.pdf)") + linkAddr <- paste0("barcode(", contrastData[i], ").pdf") + linkData <- rbind(linkData, c(linkName, linkAddr)) + dev.off() + } + } +} + +# Generate data frame of the significant differences +sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount) +if (workMode == "glm") { + + row.names(sigDiff) <- contrastData + +} else if (workMode == "classic") { + + row.names(sigDiff) <- paste0(pairData[2], "-", pairData[1]) + +} + +# Output table of summarised counts +ID <- rownames(data$counts) +outputCounts <- cbind(ID, data$counts) +write.table(outputCounts, file=countsOut, row.names=FALSE, sep="\t", + quote=FALSE) +linkName <- "Counts table (.tsv)" +linkAddr <- "counts.tsv" +linkData <- rbind(linkData, c(linkName, linkAddr)) + +# Record session info +writeLines(capture.output(sessionInfo()), sessionOut) +linkData <- rbind(linkData, c("Session Info", "session_info.txt")) + +# Record ending time and calculate total run time +timeEnd <- as.character(Sys.time()) +timeTaken <- capture.output(round(difftime(timeEnd,timeStart), digits=3)) +timeTaken <- gsub("Time difference of ", "", timeTaken, fixed=TRUE) +################################################################################ +### HTML Generation +################################################################################ +# Clear file +cat("", file=htmlPath) + +cata("\n") +HtmlHead("EdgeR Output") + +cata("\n") +cata("

    EdgeR Analysis Output:

    \n") +cata("

    Input Summary:

    \n") +if (inputType == "fastq" || inputType == "pairedFastq") { + + cata("\n") + cata(hpReadout[3], "
    \n") + cata("\n") + cata(hpReadout[8:11], sep="
    \n") + cata("
    \n") + cata("Please check that read percentages are consistent with ") + cata("expectations.
    \n") + +} else if (inputType == "counts") { + + cata("\n") + +} + +cata("The estimated common biological coefficient of variation (BCV) is: ", + commonBCV, "
    \n") + +if (secFactName == "none") { + + cata("No secondary factor specified.
    \n") + +} else { + + cata("Secondary factor specified as: ", secFactName, "
    \n") + +} + +cata("

    Output:

    \n") +cata("PDF copies of JPEGS available in 'Plots' section.
    \n") +for (i in 1:nrow(imageData)) { + if (grepl("barcode", imageData$Link[i])) { + + if (packageVersion("limma")<"3.19.19") { + + HtmlImage(imageData$Link[i], imageData$Label[i], + height=length(selectedGenes)*150) + + } else { + + HtmlImage(imageData$Link[i], imageData$Label[i], + height=length(selectedGenes)*300) + + } + } else { + + HtmlImage(imageData$Link[i], imageData$Label[i]) + + } +} +cata("
    \n") + +cata("

    Differential Representation Counts:

    \n") + +cata("\n") +cata("\n") +TableItem() +for (i in colnames(sigDiff)) { + TableHeadItem(i) +} +cata("\n") +for (i in 1:nrow(sigDiff)) { + cata("\n") + TableHeadItem(unmake.names(row.names(sigDiff)[i])) + for (j in 1:ncol(sigDiff)) { + TableItem(as.character(sigDiff[i, j])) + } + cata("\n") +} +cata("
    ") + +cata("

    Plots:

    \n") +for (i in 1:nrow(linkData)) { + if (grepl(".pdf", linkData$Link[i])) { + HtmlLink(linkData$Link[i], linkData$Label[i]) + } +} + +cata("

    Tables:

    \n") +for (i in 1:nrow(linkData)) { + if (grepl(".tsv", linkData$Link[i])) { + HtmlLink(linkData$Link[i], linkData$Label[i]) + } +} + +cata("

    Alt-click links to download file.

    \n") +cata("

    Click floppy disc icon on associated history item to download ") +cata("all files.

    \n") +cata("

    .tsv files can be viewed in Excel or any spreadsheet program.

    \n") + +cata("

    Additional Information:

    \n") + +if (inputType == "fastq") { + + ListItem("Data was gathered from fastq raw read file(s).") + +} else if (inputType == "counts") { + + ListItem("Data was gathered from a table of counts.") + +} + +if (cpmReq != 0 && sampleReq != 0) { + tempStr <- paste("Target sequences without more than", cpmReq, + "CPM in at least", sampleReq, "samples are insignificant", + "and filtered out.") + ListItem(tempStr) + + filterProp <- round(filteredCount/preFilterCount*100, digits=2) + tempStr <- paste0(filteredCount, " of ", preFilterCount," (", filterProp, + "%) target sequences were filtered out for low ", + "count-per-million.") + ListItem(tempStr) +} + +if (readReq != 0) { + tempStr <- paste("Samples that did not produce more than", readReq, + "counts were filtered out.") + ListItem(tempStr) + + tempStr <- paste0(sampleFilterCount, " samples were filtered out for low ", + "counts.") + ListItem(tempStr) +} + +if (exists("filteredSamples")) { + tempStr <- paste("The following samples were filtered out for having zero", + "library size: ", filteredSamples) + ListItem(tempStr) +} + +if (workMode == "classic") { + ListItem("An exact test was performed on each target sequence.") +} else if (workMode == "glm") { + ListItem("A generalised linear model was fitted to each target sequence.") +} + +cit <- character() +link <-character() +link[1] <- paste0("", "limma User's Guide", ".") +link[2] <- paste0("", "edgeR User's Guide", "") + +cit[1] <- paste("Robinson MD, McCarthy DJ and Smyth GK (2010).", + "edgeR: a Bioconductor package for differential", + "expression analysis of digital gene expression", + "data. Bioinformatics 26, 139-140") +cit[2] <- paste("Robinson MD and Smyth GK (2007). Moderated statistical tests", + "for assessing differences in tag abundance. Bioinformatics", + "23, 2881-2887") +cit[3] <- paste("Robinson MD and Smyth GK (2008). Small-sample estimation of", + "negative binomial dispersion, with applications to SAGE data.", + "Biostatistics, 9, 321-332") + +cit[4] <- paste("McCarthy DJ, Chen Y and Smyth GK (2012). Differential", + "expression analysis of multifactor RNA-Seq experiments with", + "respect to biological variation. Nucleic Acids Research 40,", + "4288-4297") + +cata("

    Citations

    ") +cata("
      \n") +ListItem(cit[1]) +ListItem(cit[2]) +ListItem(cit[3]) +ListItem(cit[4]) +cata("
    \n") + +cata("

    Report problems to: su.s@wehi.edu.au

    \n") + +for (i in 1:nrow(linkData)) { + if (grepl("session_info", linkData$Link[i])) { + HtmlLink(linkData$Link[i], linkData$Label[i]) + } +} + +cata("\n") +cata("\n") +TableItem("Task started at:"); TableItem(timeStart) +cata("\n") +cata("\n") +TableItem("Task ended at:"); TableItem(timeEnd) +cata("\n") +cata("\n") +TableItem("Task run time:"); TableItem(timeTaken) +cata("\n") +cata("
    \n") + +cata("\n") +cata("") diff -r 7e1ae353a1c8 -r a72211e03b38 hairpinTool.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/hairpinTool.xml Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,645 @@ + + + + for differential representation for shRNAseq and sgRNA + + + + R + shrnaseq_r + HAIRPINTOOL_R_SOURCE + + + + + + + + Rscript \$HAIRPINTOOL_R_SOURCE "$inputOpt.inputType" + #if $inputOpt.inputType=="fastq": + + #for $i, $fas in enumerate($inputOpt.fastq): + fastq::$fas.file + #end for + + "$inputOpt.hairpin" + "$inputOpt.samples" + + #if $inputOpt.positions.posOption=="yes": + $inputOpt.positions.barstart + $inputOpt.positions.barend + 0 + 0 + $inputOpt.positions.hpstart + $inputOpt.positions.hpend + #else: + 1 + 5 + 0 + 0 + 37 + 57 + #end if + #elif $inputOpt.inputType=="pairedFastq": + + #for $i, $fas in enumerate($inputOpt.fastq): + fastq::$fas.file + #end for + + #for $i, $fas in enumerate($inputOpt.fastq): + fastqRev::$fas.fileRev + #end for + + "$inputOpt.hairpin" + "$inputOpt.samples" + + #if $inputOpt.positions.posOption=="yes": + $inputOpt.positions.barstart + $inputOpt.positions.barend + $inputOpt.positions.barstartRev + $inputOpt.positions.barendRev + $inputOpt.positions.hpstart + $inputOpt.positions.hpend + #else: + 1 + 5 + 0 + 0 + 37 + 57 + #end if + + #elif $inputOpt.inputType=="counts": + "$inputOpt.counts" + "$inputOpt.hairpin" + "$inputOpt.samples" + 0 + 0 + 0 + 0 + 0 + #end if + + #if $inputOpt.secondaryFactor.secFactorOpt=="yes": + "$inputOpt.secondaryFactor.secFactName" + #else: + "none" + #end if + + #if $filterCPM.filtOption=="yes": + $filterCPM.cpmReq + $filterCPM.sampleReq + $filterCPM.readReq + #else: + -Inf + -Inf + -Inf + #end if + + "$fdr" + "$lfc" + "$direction" + "$workMode.mode" + "$outFile" + "$outFile.files_path" + + #if $workMode.mode=="classic": + "$workMode.pair1" + "$workMode.pair2" + #elif $workMode.mode=="glm": + "$workMode.contrast" + "$workMode.roast.roastOption" + + #if $workMode.roast.roastOption=="yes": + "$workMode.roast.hairpinReq" + "$workMode.roast.select.selOption" + "$workMode.roast.select.selection" + #else: + 0 + 0 + 0 + #end if + + #end if + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +.. class:: infomark + +**What it does** + +Given tables containing information about the hairpins/sgRNA and their +associated sample indices, information about the samples and fastq file +containing the sequencing reads. This tool will generate plots and tables for +the analysis of differential representation. + +.. class:: infomark + +A tutorial of how to use this tool is available at: +http://bioinf.wehi.edu.au/shRNAseq/galaxy.html + +----- + +.. class:: infomark + +**INPUTS** + +**Input File Type:** + +This tool is able to either generate counts from a raw FastQ file given the +information regarding the samples and hairpins/sgRNA. Alternatively if a table +of counts has already been generated it can also be used. + +**Counts Table (Counts Input):** + +A tab delimited text table of information regarding the counts of +hairpins/sgRNA. Should have a column 'ID' to denote the hairpins/sgRNA that +counts correspond to. Each additional column should have titles corresponding to +the label for the sample. + +Example:: + + ID Sample1 Sample2 Sample3 + Control1 49802 48014 40148 + Control2 12441 16352 14232 + Control3 9842 9148 9111 + Hairpin1 3300 3418 2914 + Hairpin2 91418 95812 93174 + Hairpin3 32985 31975 35104 + Hairpin4 12082 14081 14981 + Hairpin5 2491 2769 2691 + Hairpin6 1294 1486 1642 + Hairpin7 49501 49076 47611 + ... + +**Target Sequence Annotation:** + +A tab delimited text table of information regarding the targetted +hairpins/sgRNA sequence. Should have columns 'ID', 'Sequences' and 'Gene' to +uniquely identify the target, align it with the reads to produce counts and +identify which gene the target acts on. + +NOTE: the column names are case sensitive and should be input exactly as they +are shown here. + +Example:: + + ID Sequences Gene + Control1 TCTCGCTTGGGCGAGAGTAAG 2 + Control2 CCGCCTGAAGTCTCTGATTAA 2 + Control3 AGGAATTATAATGCTTATCTA 2 + Hairpin1 AAGGCAGAGACTGACCACCTA 4 + Hairpin2 GAGCGACCTGGTGTTACTCTA 4 + Hairpin3 ATGGTGTAAATAGAGCTGTTA 4 + Hairpin4 CAGCTCATCTTCTGTGAAGAA 4 + Hairpin5 CAGCTCTGTGGGTCAGAAGAA 4 + Hairpin6 CCAGGCACAGATCTCAAGATA 4 + Hairpin7 ATGACAAGAAAGACATCTCAA 7 + ... + +**Sample Annotation (FastQ Input):** + +A tab delimited text table of information regarding the samples. Should have +columns 'ID', 'Sequences' and 'group' to uniquely identify each sample, identify +the sample in the reads by its sample index sequence and correctly group +replicates for analysis. Additional columns may inserted for annotation purposes +and will not interfere with analysis as long as the necessary columns are +present. + +NOTE: With the exception of other_group, column names are case sensitive and +should be input exactly as they are shown here. The other_group column can be +named by the user and specified in the "Include Secondary Factor" option of the +tool. + +Example:: + + ID Sequences group other_group Replicate + 3 GAAAG Day 2 male 1 + 6 GAACC Day 10 female 1 + 9 GAAGA Day 5 GFP neg male 1 + 16 GAATT Day 5 GFP pos male 1 + 18 GACAC Day 2 female 2 + 21 GACCA Day 10 male 2 + 28 GACGT Day 5 GFP neg male 2 + 31 GACTG Day 5 GFP pos female 2 + 33 GAGAA Day 2 male 3 + 40 GAGCT Day 10 female 3 + ... + +**Include Secondary Factor** + +If there are two factors involved in the experiment (i.e. Age and Gender) then +then secondary factor should be included to improve the statistical analysis. +The secondary factor should be specified as a column in the sample annotation +file and the corresponding column name should be input exactly as it is into +the provided field in the tool. + +NOTE: Currently the secondary factor is used only to improve statistical +analysis, comparisons can only be made in the primary factor specified as +"group" in the sample annotation. + +**Specify Sample Index and Target Sequence Locations (FastQ Input):** + +It is assumed that in the sequencing reads that the first 5 bases are the +sample index sequence and that bases 37-57 are the hairpins/sgRNA. If this is +not the case then the values of the positions can be changed, however it still +requires the sample indices and hairpins/sgRNA to be in a consistent location an +in a continuous sequence. + +NOTE: position values start at 1 for the first base. + +**Filter Low CPM?:** + +Often in a large screen there may members with very low counts which are of no +interest in the experiment, these may be filtered out to speed up computations. +Filtering will be based on counts per million in a required number of samples. + +**Analysis Type:** + + * **Classic Exact Test:** This allows two experimental groups to be compared + and p-values for differential representation derivec for each target + sequence. Simple and fast for straightforward comparisons. In this option you + will have the option of "*Compare* x *To* y" which implicitly subtracts the + data from y from that of x to produce the comparison. + + * **Generalised Linear Model:** This allow for complex contrasts to be specified + and also gene level analysis to be performed. If this option is chosen then + contrasts must be explicitly stated in equations and multiple contrasts can + be made. In addition there will be the option to analyse hairpins/sgRNA on a + per-gene basis to see if hairpins/sgRNA belonging to a particular gene have + any overall tendencies for the direction of their log-fold-change. + +**FDR Threshold:** +The smear plot in the output will have hairpins/sgRNA highlighted to signify +significant differential representation. The significance is determined by +contorlling the false discovery rate, only those with a FDR lower than the +threshold will be highlighted in the plot. + + +----- + + +**Citations:** + +.. class:: infomark + +limma + +Please cite the paper below for the limma software itself. Please also try +to cite the appropriate methodology articles that describe the statistical +methods implemented in limma, depending on which limma functions you are +using. The methodology articles are listed in Section 2.1 of the limma +User's Guide. + + * Smyth, GK (2005). Limma: linear models for microarray data. In: + 'Bioinformatics and Computational Biology Solutions using R and + Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, + W. Huber (eds), Springer, New York, pages 397-420. + +.. class:: infomark + +edgeR + +Please cite the first paper for the software itself and the other papers for +the various original statistical methods implemented in edgeR. See +Section 1.2 in the User's Guide for more detail. + + * Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor + package for differential expression analysis of digital gene expression + data. Bioinformatics 26, 139-140 + + * Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing + differences in tag abundance. Bioinformatics 23, 2881-2887 + + * Robinson MD and Smyth GK (2008). Small-sample estimation of negative + binomial dispersion, with applications to SAGE data. + Biostatistics, 9, 321-332 + + * McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis + of multifactor RNA-Seq experiments with respect to biological variation. + Nucleic Acids Research 40, 4288-4297 + +Report problems to: su.s@wehi.edu.au + +.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html +.. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html + + diff -r 7e1ae353a1c8 -r a72211e03b38 readme.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/readme.rst Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,15 @@ +shrnaseq wrapper +================ + +This is a self installing Galaxy tool exposing the shrnaseq_ R package which has excellent documentation at +shrnaseq_ Minimal details are provided in this wrapper - please RTM to get the best out of it. + + +.. _shrnaseq: http://bioinf.wehi.edu.au/shRNAseq/ + +Underlying R code written by Matt Ritchie. +Galaxy wrapper by Shian Su +Autoinstallation and fixin' : Ross Lazarus + +This version first passed tests on 18 Jan 2015 + diff -r 7e1ae353a1c8 -r a72211e03b38 test-data/shrnaseq_zuber_test.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/shrnaseq_zuber_test.html Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,80 @@ + + +EdgeR Output + + +

    EdgeR Analysis Output:

    +

    Input Summary:

    + +The estimated common biological coefficient of variation (BCV) is: 0.9323
    +No secondary factor specified.
    +

    Output:

    +PDF copies of JPEGS available in 'Plots' section.
    +Counts per Index +Counts per Hairpin +MDS Plot +BCV Plot +Smear Plot(Day.0-Day.14) +
    +

    Differential Representation Counts:

    + + + + + + + + + + + + + +
    UpFlatDown
    Day 0-Day 1437668235

    Plots:

    +Counts per Index Barplot (.pdf)
    +Counts per Hairpin Barplot (.pdf)
    +MDS Plot (.pdf)
    +BCV Plot (.pdf)
    +Smear Plot(Day.0-Day.14) (.pdf)
    +

    Tables:

    +Top Tags Table(Day.0-Day.14) (.tsv)
    +Counts table (.tsv)
    +

    Alt-click links to download file.

    +

    Click floppy disc icon on associated history item to download all files.

    +

    .tsv files can be viewed in Excel or any spreadsheet program.

    +

    Additional Information:

    +
  • Data was gathered from a table of counts.
    • +
  • Target sequences without more than 0.5 CPM in at least 1 samples are insignificant and filtered out.
    • +
  • 1 of 1094 (0.09%) target sequences were filtered out for low count-per-million.
    • +
  • Samples that did not produce more than 1000 counts were filtered out.
    • +
  • 0 samples were filtered out for low counts.
    • +
  • An exact test was performed on each target sequence.
    • +

    Citations

      +
    1. Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140
      2. +
    2. Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887
      3. +
    3. Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
      4. +
    4. McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297
      5. +
    +

    Report problems to: su.s@wehi.edu.au

    +Session Info
    + + + + + + + + + + + + + +
    Task started at:2015-01-20 12:16:59
    Task ended at:2015-01-20 12:17:05
    Task run time:6 secs
    + + \ No newline at end of file diff -r 7e1ae353a1c8 -r a72211e03b38 test-data/zuber-count_matrix.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/zuber-count_matrix.txt Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,1106 @@ +ID Reads_A_T0 Reads_A_T14 Reads_B_T0 Reads_B_T14 +100043305.2 34133 9171 31158 4111 +100043305.4 5589 1 4311 5737 +100043305.5 38651 7722 24711 15331 +2900092E17Rik.377 11759 24 9328 21 +2900092E17Rik.546 3581 30 3211 3 +2900092E17Rik.1051 11498 1907 10809 4116 +2900092E17Rik.1361 5590 1128 5753 1704 +Actl6b.379 8147 3520 8262 3861 +Actl6b.819 7152 5311 7080 2636 +Actl6b.917 3549 3 3326 9 +Actl6b.989 9928 3200 12207 1787 +Actl6b.1221 2449 173 4553 190 +Alkbh2.462 8219 0 8307 3674 +Alkbh2.557 17312 11788 16162 7253 +Alkbh2.590 908 0 3 0 +Alkbh2.640 29881 9982 32507 6981 +Alkbh2.641 30677 31067 21977 5244 +Alkbh3.552 7748 95 10813 628 +Alkbh3.843 7288 47 6161 1141 +Alkbh3.901 11464 10632 13945 986 +Alkbh3.1184 235478 121880 223886 199915 +Aof2.1869 4835 1 6635 18 +Aof2.1956 9168 74 11901 2001 +Aof2.2435 22561 5296 27662 4349 +Aof2.2857 12275 0 13761 0 +Ash1l.1031 40881 12818 35694 11615 +Ash1l.1280 15907 3501 24446 2818 +Ash1l.3810 0 0 570 0 +Ash1l.8132 16135 3240 13553 8157 +Ash1l.9540 19334 4915 19956 482 +Ash2l.586 17315 3908 16296 11351 +Ash2l.805 15308 11745 19785 7504 +Ash2l.948 17154 7175 22222 1182 +Ash2l.1135 10872 23 9514 649 +Ash2l.2130 48820 71306 54356 55586 +Asxl1.2020 8883 85 7366 810 +Asxl1.3548 1499 177 4290 434 +Asxl1.3785 13925 9770 13967 1888 +Asxl1.4792 14695 2973 17368 6663 +Asxl1.5221 207 21 1015 413 +Asxl1.6315 5323 2931 4104 1254 +Asxl2.11 12636 1202 19230 836 +Asxl2.4563 6740 9470 6275 14168 +Asxl2.6593 7040 7865 7327 463 +Asxl2.7130 1005 688 1998 98 +Asxl2.7253 2848 0 4313 1015 +Asxl3.6101 27880 9120 23317 9903 +Asxl3.6824 0 0 0 0 +Asxl3.8947 10505 1283 17111 8040 +Asxl3.10021 11150 77 9094 418 +Asxl3.11070 6878 434 13876 499 +Asxl3.11073 19570 60 23697 1250 +Atm.137 7726 1 7961 0 +Atm.4098 17664 79096 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1533 +Suv420h2.825 3954 0 4272 15 +Suz12.909 19186 788 22747 876 +Suz12.1676 48602 41328 42913 5210 +Suz12.1842 20807 0 26947 0 +Suz12.3979 34551 25839 37312 26473 +Suz12.4300 95918 361295 105525 82284 +Taf1.928 29566 17762 30431 7446 +Taf1.3994 3805 11349 4811 10913 +Taf1.5030 16687 97640 11553 1419722 +Taf1.7786 26447 3893 17078 15382 +Taf3.1009 47005 40334 56864 22863 +Taf3.1315 9721 1 11094 5798 +Taf3.1769 7972 348 4563 2535 +Taf3.2951 2050 0 3001 0 +Taf3.3182 2441 4367 7877 4886 +Ube2a.142 6844 0 10490 0 +Ube2a.786 26843 3732 31512 3188 +Ube2a.1206 16679 1913 13264 2325 +Ube2a.1411 12770 1093 15872 551 +Ube2b.776 11624 596 15283 991 +Ube2b.1626 5770 228 4749 460 +Ube2b.1661 11131 10339 15579 1101 +Ube2b.2075 36359 11211 35981 12669 +Ube2b.2079 27519 36285 22274 73852 +Ube2e1.228 9870 580 7343 0 +Ube2e1.858 44362 142918 47668 36510 +Ube2e1.1041 27358 51197 25570 21613 +Ube2e1.1126 7564 45 9319 711 +Ube2e1.1207 17571 12619 15690 7099 +Ube2e1.1357 64966 13745 77653 28390 +Ube2i.40 2401 1010 2600 1494 +Ube2i.2212 62796 63531 58362 90071 +Ube2i.2447 6276 607 7252 0 +Ube2i.2498 51201 35736 51072 80735 +Usp22.1429 13262 5695 17290 9139 +Usp22.2382 31385 3450 32459 3697 +Usp22.2760 8601 267 5822 541 +Usp22.3603 1065 1 273 0 +Usp27x.1408 3564 45 4275 997 +Usp27x.2829 25035 4402 24751 8224 +Usp27x.2922 2730 1290 3453 578 +Usp27x.3592 6464 341 5536 2973 +Usp27x.3593 14001 3741 11248 384504 +Usp51.737 1039 406 562 802 +Usp51.1997 53674 23257 51654 9694 +Usp51.2109 7975 1484 8900 1269 +Usp51.2114 12165 116 13434 917 +Usp51.2194 3678 724 1318 2196 +Utx.652 13068 56560 19451 96391 +Utx.888 18640 32639 23825 9547 +Utx.1445 17137 32295 23244 29207 +Utx.4317 33766 102551 27815 12107 +Utx.4510 12851 40012 12155 17742 +Wbp7.2972 24413 60018 36070 20515 +Wbp7.3029 8164 0 11414 806 +Wbp7.5587 1840 0 2933 0 +Wbp7.6965 2142 0 1208 0 +Wdr5.501 9132 40067 12091 5193 +Wdr5.502 7780 475 6238 144 +Wdr5.1321 33084 8610 29142 6825 +Wdr5.1765 15206 0 11812 0 +Wdr5.2837 10752 1577 8922 76 +Wdr82.1889 9318 158922 6926 133595 +Wdr82.3590 102793 67621 100194 42465 +Wdr82.3705 25607 2390 22211 5706 +Wdr82.4023 47467 54245 35961 10951 +Whsc1.812 5301 8373 8384 1919 +Whsc1.818 21079 7170 18333 11013 +Whsc1.3055 20202 3 16818 4080 +Whsc1.3056 14848 3566 17067 3063 +Whsc1l1.276 8983 16 15356 86 +Whsc1l1.373 24335 1 18394 19 +Whsc1l1.524 25667 33492 20530 18722 +Whsc1l1.1307 36165 3810 43502 5067 +Whsc1l1.1653 1740 0 2619 0 +Wnt5a.2013 14662 2054 14921 8784 +Wnt5a.2659 31981 25084 30296 21463 +Wnt5a.2764 3508 402 4188 0 +Wnt5a.4154 24598 41766 25258 11415 diff -r 7e1ae353a1c8 -r a72211e03b38 test-data/zuber-sample_anno.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/zuber-sample_anno.txt Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,5 @@ +"ID" "group" +"Reads_A_T0" "Day 0" +"Reads_A_T14" "Day 14" +"Reads_B_T0" "Day 0" +"Reads_B_T14" "Day 14" diff -r 7e1ae353a1c8 -r a72211e03b38 test-data/zuber-target_anno.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/zuber-target_anno.txt Mon Jan 19 22:11:00 2015 -0500 @@ -0,0 +1,1106 @@ +ID Gene GeneID Pool shRNA_start Mean_T14.T0 T14.T0_A T14.T0_B +100043305.2 100043305 100043305 LIB 158 0.200312349 0.268684264 0.131940433 +100043305.4 100043305 100043305 LIB 369 0.665480322 0.000178923 1.330781721 +100043305.5 100043305 100043305 LIB 458 0.410099904 0.199787845 0.620411962 +2900092E17Rik.377 2900092E17Rik 67278 LIB 377 0.002146138 0.00204099 0.002251286 +2900092E17Rik.546 2900092E17Rik 67278 LIB 546 0.004655918 0.008377548 0.000934288 +2900092E17Rik.1051 2900092E17Rik 67278 LIB 1051 0.273324357 0.165854931 0.380793783 +2900092E17Rik.1361 2900092E17Rik 67278 LIB 1361 0.2489911 0.201788909 0.29619329 +Actl6b.379 Actl6b 83766 LIB 379 0.449690571 0.432060881 0.467320261 +Actl6b.819 Actl6b 83766 LIB 819 0.557452935 0.742589485 0.372316384 +Actl6b.917 Actl6b 83766 LIB 917 0.001775631 0.000845309 0.002705953 +Actl6b.989 Actl6b 83766 LIB 989 0.234356062 0.322320709 0.146391415 +Actl6b.1221 Actl6b 83766 LIB 1221 0.056185902 0.070641078 0.041730727 +Alkbh2.462 Alkbh2 231642 LIB 462 0.221138799 0 0.442277597 +Alkbh2.557 Alkbh2 231642 LIB 557 0.564841845 0.680914972 0.448768717 +Alkbh2.590 Alkbh2 231642 LIB 590 NA NA NA +Alkbh2.640 Alkbh2 231642 LIB 640 0.274406089 0.334058432 0.214753745 +Alkbh2.641 Alkbh2 231642 LIB 641 0.625663102 1.012713108 0.238613096 +Alkbh3.552 Alkbh3 69113 LIB 552 0.035169734 0.012261229 0.058078239 +Alkbh3.843 Alkbh3 69113 LIB 843 0.095823083 0.006448957 0.185197208 +Alkbh3.901 Alkbh3 69113 LIB 901 0.499065664 0.927424983 0.070706346 +Alkbh3.1184 Alkbh3 69113 LIB 1184 0.705258812 0.517585507 0.892932117 +Aof2.1869 Aof2 99982 LIB 1869 0.001459856 0.000206825 0.002712886 +Aof2.1956 Aof2 99982 LIB 1956 0.088104342 0.008071553 0.168137131 +Aof2.2435 Aof2 99982 LIB 2435 0.195980329 0.234741368 0.15721929 +Aof2.2857 Aof2 99982 LIB 2857 0 0 0 +Ash1l.1031 Ash1l 192195 LIB 1031 0.31947451 0.313544189 0.32540483 +Ash1l.1280 Ash1l 192195 LIB 1280 0.167683133 0.220091783 0.115274483 +Ash1l.3810 Ash1l 192195 LIB 3810 NA NA NA +Ash1l.8132 Ash1l 192195 LIB 8132 0.401332534 0.200805702 0.601859367 +Ash1l.9540 Ash1l 192195 LIB 9540 0.139184254 0.254215372 0.024153137 +Ash2l.586 Ash2l 23808 LIB 586 0.46112578 0.22570026 0.696551301 +Ash2l.805 Ash2l 23808 LIB 805 0.573261557 0.767245885 0.37927723 +Ash2l.948 Ash2l 23808 LIB 948 0.235730162 0.418269791 0.053190532 +Ash2l.1135 Ash2l 23808 LIB 1135 0.035165394 0.002115526 0.068215262 +Ash2l.2130 Ash2l 23808 LIB 2130 1.241609259 1.460589922 1.022628597 +Asxl1.2020 Asxl1 228790 LIB 2020 0.059766771 0.009568839 0.109964703 +Asxl1.3548 Asxl1 228790 LIB 3548 0.10962211 0.118078719 0.101165501 +Asxl1.3785 Asxl1 228790 LIB 3785 0.418395785 0.701615799 0.135175771 +Asxl1.4792 Asxl1 228790 LIB 4792 0.292975143 0.202313712 0.383636573 +Asxl1.5221 Asxl1 228790 LIB 5221 NA NA NA +Asxl1.6315 Asxl1 228790 LIB 6315 0.42809245 0.550629344 0.305555556 +Asxl2.11 Asxl2 75302 LIB 11 0.069299389 0.09512504 0.043473739 +Asxl2.4563 Asxl2 75302 LIB 4563 1.831446558 1.40504451 2.257848606 +Asxl2.6593 Asxl2 75302 LIB 6593 0.590189219 1.1171875 0.063190938 +Asxl2.7130 Asxl2 75302 LIB 7130 0.366813082 0.684577114 0.049049049 +Asxl2.7253 Asxl2 75302 LIB 7253 0.117667517 0 0.235335034 +Asxl3.6101 Asxl3 211961 LIB 6101 0.375913898 0.327116212 0.424711584 +Asxl3.6824 Asxl3 211961 LIB 6824 NA NA NA +Asxl3.8947 Asxl3 211961 LIB 8947 0.296002749 0.122132318 0.469873181 +Asxl3.10021 Asxl3 211961 LIB 10021 0.026435101 0.00690583 0.045964372 +Asxl3.11070 Asxl3 211961 LIB 11070 0.049530555 0.063099738 0.035961372 +Asxl3.11073 Asxl3 211961 LIB 11073 0.027907605 0.003065917 0.052749293 +Atm.137 Atm 11920 LIB 137 6.47E-05 0.000129433 0 +Atm.4098 Atm 11920 LIB 4098 2.922281386 4.477807971 1.366754801 +Atm.4998 Atm 11920 LIB 4998 0 0 0 +Atm.5616 Atm 11920 LIB 5616 0.443498086 0.657270086 0.229726085 +Atm.7782 Atm 11920 LIB 7782 0.260352231 0.308840206 0.211864257 +Atm.9396 Atm 11920 LIB 9396 0.60032482 0.359381765 0.841267876 +Atr.1239 Atr 245000 LIB 1239 0.005067342 0 0.010134685 +Atr.3160 Atr 245000 LIB 3160 0 0 0 +Atr.4762 Atr 245000 LIB 4762 0.149038652 0.070833921 0.227243383 +Atr.6186 Atr 245000 LIB 6186 0.128457676 0.106936416 0.149978936 +Atr.7888 Atr 245000 LIB 7888 0.147861249 0.264325323 0.031397174 +Atrx.2490 Atrx 22589 LIB 2490 0.396582444 0.600036694 0.193128195 +Atrx.5490 Atrx 22589 LIB 5490 0.356515297 0.533129237 0.179901356 +Atrx.6144 Atrx 22589 LIB 6144 4.974521294 2.266989851 7.682052736 +Atrx.7173 Atrx 22589 LIB 7173 0.05387472 0.096820809 0.01092863 +Atrx.7377 Atrx 22589 LIB 7377 0.837321391 1.529071015 0.145571768 +Aurkb.520 Aurkb 20877 LIB 520 0.008388288 0.015122511 0.001654064 +Aurkb.631 Aurkb 20877 LIB 631 0.824053234 1.06458435 0.583522118 +Aurkb.917 Aurkb 20877 LIB 917 0.024491203 0.008743956 0.04023845 +Aurkb.928 Aurkb 20877 LIB 928 0.010416667 0 0.020833333 +Aurkb.1620 Aurkb 20877 LIB 1620 0 0 0 +Aurkb.1803 Aurkb 20877 LIB 1803 0.125188349 0.175752051 0.074624647 +Baz1a.2095 Baz1a 217578 LIB 2095 0.507940486 0.356679709 0.659201262 +Baz1a.2451 Baz1a 217578 LIB 2451 2.159026811 3.284734348 1.033319275 +Baz1a.4993 Baz1a 217578 LIB 4993 0.64349492 0.507178218 0.779811623 +Baz1a.5946 Baz1a 217578 LIB 5946 0.080389068 0.05529467 0.105483466 +Baz1b.801 Baz1b 22385 LIB 801 0.000639494 0.000888362 0.000390625 +Baz1b.3683 Baz1b 22385 LIB 3683 0.360095959 0.069445862 0.650746056 +Baz1b.5646 Baz1b 22385 LIB 5646 0.016297649 0.018375115 0.014220183 +Baz1b.6371 Baz1b 22385 LIB 6371 0.14306644 0 0.286132879 +Bmi1.698 Bmi1 12151 LIB 698 0.092709454 0.069620729 0.11579818 +Bmi1.895 Bmi1 12151 LIB 895 0.035594283 0.071188567 0 +Bmi1.1440 Bmi1 12151 LIB 1440 NA NA NA +Bmi1.1746 Bmi1 12151 LIB 1746 0.118689343 0.122838849 0.114539838 +Bmi1.2404 Bmi1 12151 LIB 2404 0 0 0 +Bmi1.2561 Bmi1 12151 LIB 2561 0.454904159 0.631757214 0.278051103 +Bop1.778 Bop1 12181 LIB 778 0.672354814 0.445185419 0.899524209 +Bop1.2228 Bop1 12181 LIB 2228 0.142000285 0.127547666 0.156452904 +Bop1.2403 Bop1 12181 LIB 2403 0 0 0 +Bptf.1622 Bptf 207165 LIB 1622 0.077104921 0.08259276 0.071617083 +Bptf.3234 Bptf 207165 LIB 3234 0.057370498 0.065993919 0.048747077 +Bptf.7211 Bptf 207165 LIB 7211 0.024626408 0.021392709 0.027860107 +Bptf.8224 Bptf 207165 LIB 8224 0.410946348 0.179452295 0.6424404 +Braf.3750 Braf 109880 NC 3750 0.412381245 0.44332883 0.38143366 +Braf.3826 Braf 109880 NC 3826 0.129826198 0.1681838 0.091468596 +Braf.5053 Braf 109880 NC 5053 2.34833908 2.720656406 1.976021753 +Brd1.286 Brd1 223770 LIB 286 0.094299073 0.000374953 0.188223194 +Brd1.287 Brd1 223770 LIB 287 0.480557434 0.680387664 0.280727205 +Brd1.2096 Brd1 223770 LIB 2096 0.041243445 0.031141199 0.051345691 +Brd1.4229 Brd1 223770 LIB 4229 0.013231982 0.026463964 0 +Brd2.813 Brd2 14312 LIB 813 22.77961486 42.66701779 2.892211933 +Brd2.1714 Brd2 14312 LIB 1714 0.144143286 0.249835634 0.038450937 +Brd2.3011 Brd2 14312 LIB 3011 0.028355134 0.029962547 0.02674772 +Brd2.3396 Brd2 14312 LIB 3396 NA NA NA +Brd3.187 Brd3 67382 LIB 187 3.11E-05 0 6.22E-05 +Brd3.298 Brd3 67382 LIB 298 0.056505575 0.058248337 0.054762814 +Brd3.411 Brd3 67382 LIB 411 0.756328621 1.503010235 0.009647007 +Brd3.4162 Brd3 67382 LIB 4162 0.633054482 0.738127854 0.52798111 +Brd4.523 Brd4 57261 LIB 523 0.010380898 0.004175644 0.016586151 +Brd4.552 Brd4 57261 LIB 552 0 0 0 +Brd4.632 Brd4 57261 LIB 632 0 0 0 +Brd4.1448 Brd4 57261 LIB 1448 0.135273343 0.118396988 0.152149699 +Brd4.2097 Brd4 57261 LIB 2097 0 0 0 +Carm1.892 Carm1 59035 LIB 892 0.079435576 0.000380084 0.158491069 +Carm1.1222 Carm1 59035 LIB 1222 0.075280112 0 0.150560224 +Carm1.1454 Carm1 59035 LIB 1454 0 0 0 +Carm1.1614 Carm1 59035 LIB 1614 0.243856333 0 0.487712665 +Carm1.1717 Carm1 59035 LIB 1717 0.001731885 0.002492331 0.00097144 +Cbx1.1004 Cbx1 12412 LIB 1004 0.057581903 0.083058046 0.03210576 +Cbx1.1053 Cbx1 12412 LIB 1053 1.091733912 0.589759684 1.593708139 +Cbx1.1099 Cbx1 12412 LIB 1099 0.57419689 0.919432666 0.228961114 +Cbx1.1103 Cbx1 12412 LIB 1103 0.458716872 0.142857143 0.774576601 +Cbx1.1210 Cbx1 12412 LIB 1210 0.245937138 0.37322759 0.118646686 +Cbx2.2033 Cbx2 12416 LIB 2033 0.134848542 0.001576873 0.268120212 +Cbx2.3537 Cbx2 12416 LIB 3537 0 0 0 +Cbx2.3545 Cbx2 12416 LIB 3545 0.710820484 1.275496689 0.146144279 +Cbx2.3620 Cbx2 12416 LIB 3620 0.134612189 0.008686441 0.260537937 +Cbx2.3706 Cbx2 12416 LIB 3706 1.147329284 1.49221144 0.802447127 +Cbx3.882 Cbx3 12417 LIB 882 0 0 0 +Cbx3.1206 Cbx3 12417 LIB 1206 0.118944773 0.131788129 0.106101417 +Cbx3.1339 Cbx3 12417 LIB 1339 0.142134201 0.070435009 0.213833393 +Cbx3.1719 Cbx3 12417 LIB 1719 0 0 0 +Cbx3.1735 Cbx3 12417 LIB 1735 0.115344034 0.011380552 0.219307515 +Cbx4.1794 Cbx4 12418 LIB 1794 1.364806716 0.371060327 2.358553106 +Cbx4.1844 Cbx4 12418 LIB 1844 0.700301215 1.223163842 0.177438588 +Cbx4.2573 Cbx4 12418 LIB 2573 0.817611719 0.890839629 0.744383809 +Cbx4.4403 Cbx4 12418 LIB 4403 0.215720626 0 0.431441252 +Cbx4.5120 Cbx4 12418 LIB 5120 0.497429027 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0.358981494 0.558914978 +Padi4.1914 Padi4 18602 LIB 1914 0.242089806 0.484052223 0.000127389 +Padi4.2336 Padi4 18602 LIB 2336 0.062117231 0.059119919 0.065114542 +Parp1.1124 Parp1 11545 LIB 1124 0.210888138 0.367549038 0.054227238 +Parp1.1623 Parp1 11545 LIB 1623 0.104470818 0.188126755 0.02081488 +Parp1.2887 Parp1 11545 LIB 2887 0.892550544 0.316455696 1.468645392 +Parp1.3789 Parp1 11545 LIB 3789 0.015223099 0.029524865 0.000921332 +Parp1.3790 Parp1 11545 LIB 3790 0.362609031 0.342329793 0.382888269 +Parp2.574 Parp2 11546 LIB 574 0.006105603 0.012211205 0 +Parp2.905 Parp2 11546 LIB 905 0.247891643 0.495783286 0 +Parp2.946 Parp2 11546 LIB 946 0.053243848 0.106487696 0 +Parp2.1006 Parp2 11546 LIB 1006 1.662743561 1.577856302 1.74763082 +Parp2.1429 Parp2 11546 LIB 1429 0.168234787 0.308182513 0.028287061 +Parp2.1677 Parp2 11546 LIB 1677 0.030777532 0.017032993 0.04452207 +Pax5.154 Pax5 18507 LIB 154 0.16139052 0.235338822 0.087442219 +Pax5.328 Pax5 18507 LIB 328 0.39397106 0.498357756 0.289584364 +Pax5.601 Pax5 18507 LIB 601 0.048479251 0.024589675 0.072368828 +Pax5.852 Pax5 18507 LIB 852 9.678858441 7.766500178 11.5912167 +Pax5.914 Pax5 18507 LIB 914 0.152719984 0.030076142 0.275363825 +Paxip1.3203 Paxip1 55982 LIB 3203 0.187420804 0.303786616 0.071054993 +Paxip1.3451 Paxip1 55982 LIB 3451 3.865093664 3.578045861 4.152141468 +Paxip1.3562 Paxip1 55982 LIB 3562 0.722329293 0.613098514 0.831560072 +Paxip1.3670 Paxip1 55982 LIB 3670 5.124811105 9.197264873 1.052357337 +Pcgf2.507 Pcgf2 22658 LIB 507 0.35115 0.32717803 0.375121969 +Pcgf2.690 Pcgf2 22658 LIB 690 0.084288102 0.160556564 0.00801964 +Pcgf2.801 Pcgf2 22658 LIB 801 0.09993849 0.068596738 0.131280242 +Pcgf2.982 Pcgf2 22658 LIB 982 0.444925531 0.384459989 0.505391072 +Pcgf2.1013 Pcgf2 22658 LIB 1013 0.090092787 5.67E-05 0.180128878 +Pcmt1.619 Pcmt1 18537 LIB 619 1.638746981 2.371052632 0.90644133 +Pcmt1.620 Pcmt1 18537 LIB 620 0.066119686 0.000258465 0.131980907 +Pcmt1.840 Pcmt1 18537 LIB 840 27.42358533 24.71543731 30.13173335 +Pcmt1.946 Pcmt1 18537 LIB 946 0.678912333 0.407078398 0.950746269 +Pcmt1.1259 Pcmt1 18537 LIB 1259 0.610136449 0.807828401 0.412444497 +Pcna.566 Pcna 18538 PC 566 0.013756331 0.021430795 0.006081866 +Pcna.1186 Pcna 18538 PC 1186 0 0 0 +Phf1.561 Phf1 21652 LIB 561 0.571926578 1.105404769 0.038448387 +Phf1.637 Phf1 21652 LIB 637 0.33142531 0.459700889 0.203149731 +Phf1.1098 Phf1 21652 LIB 1098 0.118527608 0.184246062 0.052809154 +Phf1.2400 Phf1 21652 LIB 2400 0.178047503 0.204710012 0.151384993 +Phf2.1851 Phf2 18676 LIB 1851 0.281793921 0.46524031 0.098347533 +Phf2.3668 Phf2 18676 LIB 3668 0.352946923 0.001776199 0.704117647 +Phf2.5132 Phf2 18676 LIB 5132 1.021354121 0.675807289 1.366900952 +Phf2.5166 Phf2 18676 LIB 5166 1.690348646 0.566009654 2.814687638 +Phf8.1420 Phf8 320595 LIB 1420 0.539886389 0.513086274 0.566686504 +Phf8.1984 Phf8 320595 LIB 1984 0.090594691 0.06110417 0.120085213 +Phf8.2131 Phf8 320595 LIB 2131 0.020983494 0.041486603 0.000480384 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100042784 LIB 1844 0.049836641 0.003384095 0.096289187 +Prdm11.2306 Prdm11 100042784 LIB 2306 0.106674086 0.187523208 0.025824964 +Prdm11.3215 Prdm11 100042784 LIB 3215 0.248260853 0.357676324 0.138845382 +Prdm11.3216 Prdm11 100042784 LIB 3216 0.162689304 0.102281495 0.223097113 +Prdm12.481 Prdm12 381359 LIB 481 0.121026312 9.03E-05 0.241962372 +Prdm12.603 Prdm12 381359 LIB 603 0.11654652 0.146549398 0.086543642 +Prdm12.1421 Prdm12 381359 LIB 1421 0.036882594 0.001164353 0.072600834 +Prdm12.2351 Prdm12 381359 LIB 2351 0.862186131 0.987938749 0.736433513 +Prdm13.554 Prdm13 230025 LIB 554 0.060698903 0.02212727 0.099270536 +Prdm13.2613 Prdm13 230025 LIB 2613 5.270386453 3.099350703 7.441422202 +Prdm13.2702 Prdm13 230025 LIB 2702 0.521784232 0 1.043568465 +Prdm13.2828 Prdm13 230025 LIB 2828 0.075151234 0.112102387 0.038200081 +Prdm13.2936 Prdm13 230025 LIB 2936 0.269317079 0.20276676 0.335867398 +Prdm14.530 Prdm14 383491 LIB 530 0.405412499 0.39411959 0.416705408 +Prdm14.1756 Prdm14 383491 LIB 1756 0.005945122 0 0.011890244 +Prdm14.1934 Prdm14 383491 LIB 1934 0.293485392 0.527073733 0.059897052 +Prdm14.2271 Prdm14 383491 LIB 2271 0.111037316 0.097569355 0.124505277 +Prdm14.2348 Prdm14 383491 LIB 2348 0.492448295 0.042176871 0.94271972 +Prdm15.958 Prdm15 114604 LIB 958 0.98252896 1.932489451 0.032568468 +Prdm15.1034 Prdm15 114604 LIB 1034 0.004062756 0.003733333 0.004392179 +Prdm15.1690 Prdm15 114604 LIB 1690 0.524906994 0.429446747 0.620367241 +Prdm15.1796 Prdm15 114604 LIB 1796 0.174355348 0.183572542 0.165138154 +Prdm15.2734 Prdm15 114604 LIB 2734 0.038957255 0.003943577 0.073970934 +Prdm16.680 Prdm16 70673 LIB 680 0.533914395 0.660762545 0.407066246 +Prdm16.5683 Prdm16 70673 LIB 5683 0.096716361 0.180946552 0.01248617 +Prdm16.8212 Prdm16 70673 LIB 8212 NA NA NA +Prdm16.8363 Prdm16 70673 LIB 8363 0.430720448 0.248793956 0.61264694 +Prdm16.8571 Prdm16 70673 LIB 8571 2.329170058 2.550566982 2.107773134 +Prdm2.1020 Prdm2 110593 LIB 1020 0.153596644 0.259297344 0.047895945 +Prdm2.4191 Prdm2 110593 LIB 4191 0.559676498 0.502441293 0.616911704 +Prdm2.4655 Prdm2 110593 LIB 4655 NA NA NA +Prdm2.4956 Prdm2 110593 LIB 4956 0.090352843 0.160106435 0.020599251 +Prdm2.5781 Prdm2 110593 LIB 5781 0.2845147 0.353298153 0.215731247 +Prdm4.1943 Prdm4 72843 LIB 1943 0.115287664 0.047414054 0.183161274 +Prdm4.2535 Prdm4 72843 LIB 2535 0.085408022 0.000193199 0.170622844 +Prdm4.3139 Prdm4 72843 LIB 3139 0.001128668 0 0.002257336 +Prdm4.3391 Prdm4 72843 LIB 3391 0.069371374 0.05967095 0.079071797 +Prdm4.3667 Prdm4 72843 LIB 3667 NA NA NA +Prdm5.908 Prdm5 70779 LIB 908 0.149313429 0.128342246 0.170284612 +Prdm5.984 Prdm5 70779 LIB 984 0.413409055 0.622305737 0.204512373 +Prdm5.1353 Prdm5 70779 LIB 1353 1.643969827 2.094062103 1.193877551 +Prdm5.2148 Prdm5 70779 LIB 2148 0.648213784 0.715798492 0.580629076 +Prdm6.1018 Prdm6 225518 LIB 1018 0.009633756 0.011159403 0.008108108 +Prdm6.1790 Prdm6 225518 LIB 1790 0.123211194 0.098809823 0.147612565 +Prdm6.1823 Prdm6 225518 LIB 1823 0.328175562 0.377128018 0.279223106 +Prdm6.1839 Prdm6 225518 LIB 1839 0.201249487 0.337440758 0.065058216 +Prdm8.2290 Prdm8 77630 LIB 2290 0.000542104 0.00048216 0.000602047 +Prdm8.3042 Prdm8 77630 LIB 3042 3.233747051 5.976620158 0.490873944 +Prdm8.3048 Prdm8 77630 LIB 3048 0.640755782 1.166720727 0.114790837 +Prdm8.3062 Prdm8 77630 LIB 3062 0.361793005 0.256726293 0.466859717 +Prdm9.77 Prdm9 213389 LIB 77 0.401141561 0.283516221 0.518766901 +Prdm9.496 Prdm9 213389 LIB 496 0.222497616 0.078468537 0.366526695 +Prdm9.1838 Prdm9 213389 LIB 1838 0.302877526 0.400302009 0.205453043 +Prkaa1.145 Prkaa1 105787 LIB 145 0.14165733 0.1690104 0.114304261 +Prkaa1.1981 Prkaa1 105787 LIB 1981 0.019533353 0.019407151 0.019659554 +Prkaa1.3080 Prkaa1 105787 LIB 3080 NA NA NA +Prkaa1.3635 Prkaa1 105787 LIB 3635 3.189719542 4.278249106 2.101189977 +Prkaa1.3954 Prkaa1 105787 LIB 3954 0.013169577 0 0.026339154 +Prkaa1.4596 Prkaa1 105787 LIB 4596 0.034088344 0.058964593 0.009212095 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